A key problem in neuroscience is to uncover fundamental principles and algorithms that allow neuronal networks to perform the complex calculations that underlie normal behavior effectively and efficiently. Our goal in this project is to develop a powerful team approach to understand the mechanisms and principles underlying the processing of odorant information by the olfactory system. In Project 1, we will develop the experimental and computational methods to characterize the processing of olfactory information by the olfactory bulb, and to examine how that processing changes with learning or changes in neuronal excitability. In Project 2, we will develop real time approaches to the analyses developed in Project 1 to improve our ability to test hypotheses of information encoding in the olfactory bulb, and the roles of specific neurons in this encoding. In Project 3, we will perform neural network modeling of the olfactory bulb circuit in a way that allows rapid and global optimization of parameters that have biophysical relevance for understanding bulb circuitry function. In Project 4, we will develop dual in vivo imaging of cortex and olfactory bulb to better understand the transfer of olfactory information to the cortex and how this changes with learning. In Project 5, we will examine the cortical feedback to the bulb to understand how this feedback helps shape olfactory odorant responses and direct the appropriate changes in bulb circuitry during learning that will preserve odor information. The technical innovation of this proposal is driven by a multilevel experimental approach that leverages expertise from an investigative team with diverse backgrounds. We expect this approach to uncover valuable insights into principles of neural circuit organization and algorithmic function that underlie olfactory system function and plasticity. Our studies will have a broad impact on our understanding of how neuronal circuits implement effective algorithms, and will likely provide important insights into the function of other circuits in the central nervous system. In addition, our work may reveal previously unrecognized computational algorithms that will have a broad impact on computer science.